11 ms·

When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give y

• lelanthran 4mo ago
  > When discussing LLM pricing, people are missing the plot. [ ... snipped ...] Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. And you think it is unreasonable to consider this unsustainable?
  • z2 4mo ago
    And the direction is definitely towards removing that subsidy really soon. We can see it with OpenAI's shift to API-equivalent pricing for enterprise customers last month. Anecdotally my company saw OpenAI credit usage grow 2x with stable use across the ChatGPT platform, which is pretty terrifying considering just 2% of the company uses Codex. For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la carte at inflated 1.75x rates vs API, or if you don't want to pay, you get access to anything but the non-reasoning models turned off for the month. We also tried Claude Enterprise, which was unusable as people blew through their monthly limits in a matter of hours.
  • wongarsu 4mo ago
    Depends on what their actual costs are. Either they are losing lots of money on subscriptions, or they make absolute bank on API pricing. Looking at the pricing of 1-2T models like Kimi or DeepSeek on the open market, I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing. Especially considering that subscriptions a) distribute load over time via rate limits, and b) will include a lot of users who get only a fraction of the possible value, whether they are on a personal account where they are on the rate limit on the weekend but barely use it during the week, or are corporate users who were issued an account they rarely use. Subscription prices are usually measured on the average case, not the most extreme value a power user can get out of it
    • Forgeties79 4mo ago
      Considering not one company is in the black yet I don’t really know how we can say anyone is making bank, unless we want to count absurd levels of VC funding (now slowing down) I guess.
      • wongarsu 4mo ago
        I am conveniently not counting training costs (since they add no marginal costs, selling more tokens doesn't impact them), and hardware and DC costs only amortized Of course they do have to "make bank" in some way to offset the insane training costs. But whether they go for high prices or high volume, or offer some services as a loss leader to drive profits elsewhere is somewhat orthogonal to that
      • anthonypasq 4mo ago
        https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-propel-anthropic-into-its-first-profitable-quarter-7edbf2f4 https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-...
        • Forgeties79 4mo ago
          Let’s see it first. And without omitting training/infrastructure costs at that. Until then my comment is still accurate.
          • anthonypasq 4mo ago
            its a private company, what exactly do you expect to 'see'?
            • Forgeties79 4mo ago
              Anthropic IPO's in less than 5 months and I guarantee you any company that officially is in the black will proudly shout it from the rooftops.
• stingraycharles 4mo ago
  Also, your local hardware is in no way capable of running the types of models that the cloud providers do, it’s just not economically feasible, and it never will be.
  • zozbot234 4mo ago
    It can run open-weight models that are roughly as capable. It's going to be slow unless you're using actual datacenter hardware, but they'll run.
    • colonCapitalDee 4mo ago
      "roughly" is doing a lot of heavy lifting there
      • adrian_b 4mo ago
        The difference between datacenter hardware and cheap personal hardware is not in what can be run and what cannot be run. Anything can also be run on a cheap computer. The difference is in speed. A cheap computer may run a big model up to a few orders of magnitude slower than datacenter hardware, depending on whether the LLM is small enough to fit in GPU memory, or it is small enough to fit in CPU memory or it is so big that it must spill on SSDs. Depending on the application, the tradeoff between run time and run cost may happen to favor using local hardware, despite a much slower speed. There are plenty of applications where doing them for negligible cost during an overnight job can be preferable to obtaining faster results at a very high price, for instance scanning for bugs in a mature code base using a great number of different open-weights LLMs, which can achieve similar bug coverage like using a single, but overpriced and unavailable SOTA LLM, e.g. Mythos.
        • stingraycharles 4mo ago
          > The difference between datacenter hardware and cheap personal hardware is not in what can be run and what cannot be run. You do realize that a model like Opus is (estimated to be) around 5T parameters, and uses around 5TB of GPU memory? These kind of things are just impossible to run locally.
• simonw 4mo ago
  I learned today that the Anthropic "Enterprise" plan - the one big companies use because they need governance features and audit logs and all of that jazz - is billed at API token rates (plus $20/seat/month). So large companies are getting billed a lot more than those discount subscription plans.
  • alexriddle 4mo ago
    Anything over 150 seats means you need to pay at token rates plus the $20/user. My day job is operational (no coding at all) and I'm spending ~$300 a month on a few chats with Claude/Cowork a day over the course of a month.
    • stymaar 4mo ago
      I hope your company is keeping the input/response pair in case they need to break free at some point.
      • dd8601fn 4mo ago
        Wouldn’t people mostly just want any artifacts?
        • speed_spread 4mo ago
          Like Slack history, LLM history can be used to build searchable knowledge base. Questions are often more valuable than answers.
    • m_kos 4mo ago
      $300 is my employer's monthly cap on Claude Enterprise. It lasts me at most a week of moderate use. I would much rather get Codex Pro and Claude Pro or Max, which would cost ≤ $200. For $300, one could also add Gemini Ultra to the mix so I could have all three review each other's code, etc. Claude can be very good but enterprise pricing doesn't make sense to me.
      • 4mo ago
• cyanydeez 4mo ago
  Isn't the plot that it's like an infinite bikeshed but 10% of the biksheds are actually trailer parks and when you finally realize it's a trailer park and not a bike shed you're down 10-100$ because it's token gen is faster than you can actually validate? Some might say the price wouldn't be great if you could actually process and validate it...
• kelseyfrog 4mo ago
  > The quality of the model “operator” makes a massive difference in the outcomes. My hunch is that this is the source of much of the variability in outcomes upstream of HN commenters claiming extremes of, "This model changes everything!" to "This[same] model is crap." We haven't operationalized what it means to "be good at prompting," nor developed proxies/heuristics/shibboleths for accessing prompting skill. There's community skepticism over whether prompting skill even exists. Besides even if prompting skill is real, who wants to hear, "Actually you kinda suck at prompting."
  • danielmarkbruce 4mo ago
    It's 100% this. Many people suck at prompting. It's likely that habits from search are ingrained. But in general some people are just so bad at it .
    • latexr 4mo ago
      According to Google, “there’s no wrong way to prompt”. https://www.youtube.com/watch?v=9bBfYX8X5aU&t=48s https://www.youtube.com/watch?v=9bBfYX8X5aU&t=48s
      • knollimar 4mo ago
        No wrong way to [consume thing I sell that you'll consume more of if you do it poorly]
      • djeastm 4mo ago
        Ehhh, their incentive in their marketing is to get normal people to not be intimidated by the big bad AI. Power users are always going to have to take the messaging companies send out to the masses with a grain of salt.
      • redsocksfan45 4mo ago
        [dead]
    • jyounker 4mo ago
• stymaar 4mo ago
  > Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus What's your source for Opus being a 5T model? > and tiny distillations from DeepSeek that perform well only in benchmarks. I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”. And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decent model (obviously it's not Opus, but it's also much faster and speed is a quality of its own).
  • gpugreg 4mo ago
    > What's your source for Opus being a 5T model? Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m https://xcancel.com/elonmusk/status/2042123561666855235#m While this source's reliability is certainly debatable, the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/ https://01.me/research/ikp/
    • stymaar 4mo ago
      > While this source's reliability is certainly debatable Massive understatement. Nowadays it has become hard to find a single Musk statement that doesn't contain at least one lie. > the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/ https://01.me/research/ikp/ Thanks for the pointer. This estimation has Grok 6 times bigger than Musk claims it is, so maybe that's where the lie is. (I'm quite skeptical about that number though, it would be quite disappointing for the US tech if their flagship models had to be that much larger than the Chinese ones for such a small edge in performance. Because I don't think US labs are incompetent, I'd bet that US flagships aren't more than 2/3 times bigger than Chinese flagship. Otherwise it really doesn't bode well.)
      • striking 4mo ago
        In tiny gray text right above the table is written "90% PI ≈ ±3.00× either side." Is GPT-5.5-Pro 3.4T or 30.8T in size, or somewhere in between? We just don't know.
• runtime_terror 4mo ago
  > The subscription token price is 10x-40x cheaper than API pricing This is a temporary phenomenon. Expect either drastic price increases or draconian throttling or both in the coming months. These companies are operating at huge loses and have hundreds of billions in liabilities and commitments. They need to turn on the money faucet sooner than later.
  • anthonypasq 4mo ago
    Theres recent reporting that Anthropic will be profitable this quarter... edit: I see in other comments on this thread you think Ed Zitron is a reliable pundit so that explains everything.
    • runtime_terror 4mo ago
      How will it be profitable, really? You can dismiss Ed (and me vicariously) but what's your compelling evidence to counter their extremely uphill battle towards profitability? Either way it will be very interesting to see their S1 when they try and IPO. If it's anything like SpaceX's then I suspect my post will age better than yours.
      • brookst 4mo ago
        I sincerely doubt Anthropic’s IPO will say that their AI business is only 2% of their future revenue, and they’re bundling in totally unrelated, unprofitable things they expect to account for 98%.
        • runtime_terror 4mo ago
          I'm not sure what you're talking about or referring to... I haven't heard anyone claim their S1 will show that but that it will show how poorly their revenue figures look against their costs.
          • brookst 4mo ago
• xbmcuser 4mo ago
  Its not like the non frontier are not improving. If someone can use deepseek to get 90% of the work done for $100 then pay another $100 to anthropic or openai to complete it I think they will rather do that than pay anthropic or openai for $1000.
  • jonfromsf 4mo ago
    Yes, for indie developers and small startups. Large corps won't want their code /email/etc data being looked at by the Chinese government.
    • LUmBULtERA 4mo ago
      For Deepseek and other openweight models, you can use non-Chinese hosted infrastructure that offer zero data retention and still save a whole lot of money. A large corp could even host their own Deepseek v4.0 Flash model internally for some basic work.
• try-working 4mo ago
  DeepSeek and Xiaomi are so cheap there's no need to get a plan. Just use the API.
  • jason_s 4mo ago
    something something something China something something intellectual property something something....
    • noman-land 4mo ago
      You can just say the words instead of implying their meaning and letting everyone fill in the gaps themselves.
• protocolture 4mo ago
  >When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. These are loss leaders that will not be maintained over the long term. Already we see moves to restrict their usage and redirect people back to API pricing.
• otabdeveloper4 4mo ago
  The subscription plans are the "first hit is free" plans. They're not gonna last and don't build anything serious based on them.
• onesingleblast 4mo ago
  So when you need an LLM in your backend, scrape Claude Code instead of using the API :)
• johalmed 4mo ago
  [flagged]